Conventional systems allow for face detection to be performed in many ways. These conventional systems, however, often mischaracterize non-facial objects as faces. As a result, processing of the image data may be slowed and/or processing power may be increased.
Currently, there are a number of different methods for face tracking. Most of these methods attempt to reduce processing power by limiting the search area for face detection. Other methods use object matching filtering techniques instead of the face detection techniques. Yet other methods reduce the face searching area by pre-filtering the image data. However, the processing power consumption corresponding to the use of these techniques remains very high. Some of these techniques suffer from slow detection of new faces appearing in the image scene because the search area is limited. In addition, some of these techniques are rendered ineffective when there are a large number of faces in the image area, as they cannot accurately process the large number of faces.
Thus, there is a need in the art for systems and methods of improved face tracking to reduce power consumption and increase processing speeds.